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Clustering is a common technique for statistical data analysis, Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very…

Machine Learning · Computer Science 2012-03-12 T Soni Madhulatha

In this paper, we introduce a geometric statistic called the "sprawl" of a group with respect to a generating set, based on the average distance in the word metric between pairs of words of equal length. The sprawl quantifies a certain…

Group Theory · Mathematics 2016-01-20 Moon Duchin , Samuel Lelièvre , Christopher Mooney

A prominent parameter in the context of network analysis, originally proposed by Watts and Strogatz (Collective dynamics of `small-world' networks, Nature 393 (1998) 440-442), is the clustering coefficient of a graph $G$. It is defined as…

Combinatorics · Mathematics 2016-11-21 Michael Gentner , Irene Heinrich , Simon Jäger , Dieter Rautenbach

We deal with the random combinatorial structures called assemblies. By weakening the logarithmic condition which assures regularity of the number of components of a given order, we extend the notion of logarithmic assemblies. Using the…

Probability · Mathematics 2009-03-06 Eugenijus Manstavičius

Compositionality in language refers to how much the meaning of some phrase can be decomposed into the meaning of its constituents and the way these constituents are combined. Based on the premise that substitution by synonyms is…

Computation and Language · Computer Science 2017-03-13 Christina Lioma , Niels Dalum Hansen

In this chapter we review some examples, methods, and recent results involving comparison of clustering properties of point processes. Our approach is founded on some basic observations allowing us to consider void probabilities and moment…

Probability · Mathematics 2014-05-23 Bartłomiej Błaszczyszyn , D. Yogeshwaran

Let $X_1,\ldots,X_n$ be a sequence of independent random points in $\mathbb{R}^d$ with common Lebesgue density $f$. Under some conditions on $f$, we obtain a Poisson limit theorem, as $n \to \infty$, for the number of large probability…

Probability · Mathematics 2021-05-04 Nicolas Chenavier , Norbert Henze , Moritz Otto

A permutation statistic $\operatorname{st}$ is said to be shuffle-compatible if the distribution of $\operatorname{st}$ over the set of shuffles of two disjoint permutations $\pi$ and $\sigma$ depends only on $\operatorname{st}\pi$,…

Combinatorics · Mathematics 2023-09-29 Jinting Liang , Bruce E. Sagan , Yan Zhuang

The problem of time-series clustering is considered in the case where each data-point is a sample generated by a piecewise stationary ergodic process. Stationary processes are perhaps the most general class of processes considered in…

Machine Learning · Statistics 2019-06-27 Azadeh Khaleghi , Daniil Ryabko

We consider the problem of inferring the probability distribution associated with a language, given data consisting of an infinite sequence of elements of the languge. We do this under two assumptions on the algorithms concerned: (i) like a…

Machine Learning · Computer Science 2014-07-16 Paul M. B. Vitanyi , Nick Chater

Oriented closed curves on an orientable surface with boundary are described up to continuous deformation by reduced cyclic words in the generators of the fundamental group and their inverses. By self-intersection number one means the…

Geometric Topology · Mathematics 2011-08-03 Moira Chas , Steven P. Lalley

Infinite mixture models are commonly used for clustering. One can sample from the posterior of mixture assignments by Monte Carlo methods or find its maximum a posteriori solution by optimization. However, in some problems the posterior is…

Machine Learning · Computer Science 2013-11-26 Işık Barış Fidaner , Ali Taylan Cemgil

The first part of this work considers the entropy of the sum of (possibly dependent and non-identically distributed) Bernoulli random variables. Upper bounds on the error that follows from an approximation of this entropy by the entropy of…

Information Theory · Computer Science 2013-04-30 Igal Sason

A new maximum approximate likelihood (ML) estimation algorithm for the mixture of Kent distribution is proposed. The new algorithm is constructed via the BSLM (block successive lower-bound maximization) framework and incorporates manifold…

Computation · Statistics 2017-09-15 Hien D. Nguyen

Kim defined a very general combinatorial abstraction of the diameter of polytopes called subset partition graphs to study how certain combinatorial properties of such graphs may be achieved in lower bound constructions. Using Lov\'asz'…

Combinatorics · Mathematics 2012-03-08 Nicolai Hähnle

A popular method for selecting the number of clusters is based on stability arguments: one chooses the number of clusters such that the corresponding clustering results are "most stable". In recent years, a series of papers has analyzed the…

Machine Learning · Statistics 2010-07-08 Ulrike von Luxburg

A given subset $A$ of natural numbers is said to be complete if every element of $\mathbb{N}$ is the sum of distinct terms taken from $A$. This topic is strongly connected to the knapsack problem which is known to be NP complete.…

Combinatorics · Mathematics 2023-04-05 Norbert Hegyvári

Convergence of order $O(1/\sqrt{n})$ is obtained for the distance in total variation between the Poisson distribution and the distribution of the number of fixed size cycles in generalized random graphs with random vertex weights. The…

Probability · Mathematics 2024-05-31 Sergey G. Bobkov , Maria A. Danshina , Vladimir V. Ulyanov

We study the joint distribution of the number of occurrences of members of a collection of nonoverlapping motifs in digital data. We deal with finite and countably infinite collections. For infinite collections, the setting requires that we…

Probability · Mathematics 2016-07-05 Jeffrey Gaither , Hosam Mahmoud , Mark Daniel Ward

Sampling formulas describe probability laws of exchangeable combinatorial structures like partitions and compositions. We give a brief account of two known parametric families of sampling formulas and add a new family to the list.

Probability · Mathematics 2007-05-23 Alexander Gnedin
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